The availability of massive amount of dam safety monitoring data can make it difficult to analyze and characterize dam behavior. This article describes the use of the Cloud model to transform quantitative monitoring data into qualitative information. Each monitoring point returning dam safety data is regarded as a cloud drop, and parameters such as the expectation, entropy, and hyper-entropy of the monitoring data are obtained through a backward cloud generator to represent the operational state of the dam. The monitoring points are then treated as vectors, and the cloud similarity is calculated using the cosine value of the angle between them. The cloud similarity coefficient is then determined to characterize the similarity of dam behavior. Experimental analysis shows that the process of identifying cloud parameters has a good effect on the discovery of abnormal monitoring values regarding dam safety and demonstrates the feasibility of characterizing the dam behavior. Clustering analysis is applied to the similarity coefficients to further achieve the hierarchical management of dam monitoring points.

中文翻译：

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基于Cloud模型的多监测点大坝行为相似性分析

大量大坝安全监测数据的可用性使得分析和表征大坝行为变得困难。本文介绍了使用云模型将定量监测数据转化为定性信息。将每个返回大坝安全数据的监测点视为一个云滴，通过后向云生成器得到监测数据的期望、熵、超熵等参数来表示大坝的运行状态。然后将监测点作为向量处理，利用它们之间夹角的余弦值计算云相似度。然后确定云相似系数来表征大坝行为的相似性。实验分析表明，云参数识别过程对发现大坝安全异常监测值具有良好的效果，证明了表征大坝行为的可行性。对相似系数进行聚类分析，进一步实现大坝监测点的分级管理。